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AI Architect

Zensar
Pune, IND
Senior · 10+ years experience
Onsite
Discovered 2 weeks ago
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Qualifications

  • 10+ years of software architecture experience, with at least 3 years building production‐grade LLM applications.
  • Deep expertise in multi‐agent frameworks such as LangGraph, AutoGen, or CrewAI.
  • Proven experience designing or implementing Model Context Protocol (MCP) or similar context‐sharing protocols.
  • Strong command of Python, TypeScript, or Go and cloud‐native technologies (AWS/GCP/Azure, Kubernetes, Docker).
  • Hands‐on experience with prompt engineering, Retrieval‐Augmented Generation (RAG) pipelines, and vector databases.
  • Demonstrated ability to produce high‐quality, production‐ready code and prototypes.
  • Outstanding verbal and written communication skills, with a track record of leading technical discussions.
  • Experience authoring architecture RFCs and establishing engineering standards.
  • Ability to translate complex AI concepts into actionable solutions for business stakeholders.
  • Bachelor’s or higher degree in Computer Science, Engineering, or a related field.

Responsibilities

  • Design and implement multi‐agent orchestration frameworks for autonomous negotiation, task delegation, and state management.
  • Architect and develop scalable Model Context Protocol (MCP) servers and clients for secure LLM integration with data sources and enterprise tools.
  • Define the architectural vision for Zensar's internal AI platform, ensuring high throughput, low latency, and seamless developer workflows.
  • Write production‐ready code, build prototypes, and establish engineering best practices for AI application development.
  • Standardize architectural patterns across the organization and author comprehensive RFCs.
  • Mentor junior engineers and foster a culture of technical excellence.
  • Collaborate with product, security, and operations teams to align AI solutions with business goals.
  • Evaluate and integrate emerging AI SDKs, vector databases, and Retrieval‐Augmented Generation (RAG) pipelines.
  • Drive performance tuning, scalability testing, and reliability engineering for AI services.
  • Communicate complex technical concepts clearly to both technical and non‐technical stakeholders.

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